[R-sig-ME] FW: Prediction of random effects - logist mixed model

[Correa S.T.]

Dear list,

I am working with a two-level logistic mixed model and I am interested
in predicting the random effects for a given value of the parameters
(not for the estimates obtained from the data at hand, which can be
obtaiend using fucntion 'ranef'). For illustration, please see the code
below.

# mixed logistic model

fit1.b<-lmer(yij.b.true  ~ x1 + x2 + (1|area), family =
binomial(link=logit), data=bootsamp
,control=list(usePQL=FALSE),verbose=FALSE,method="Laplace")
 
# estimates of the parameter based on the data at hand

beta.hat<-fixef(fit1.b)
varu.hat<-as.numeric(VarCorr(fit1.b)[[1]][1,1])

# predicting group effects based on the data at hand

u.pred<-ranef(fit1.b)[[1]

==>> I would like to obtain u.pred2 such that u.pred2=g(beta, varu) for
any given beta and 

varu. In other words, I need to extract the function g() used in the
lmer to predict the u random effects. Is there a way to do that?

Thank you very much.

Solange Correa,
Ph.D. student
Social Statistics
University of Southampton, UK.


************* ******************************** 
summary of the model fitting
**********************************************

> fit1.b
Generalized linear mixed model fit using Laplace
Formula: yij.b.true ~ x1 + x2 + (1 | area) 
   Data: bootsamp
 Family: binomial(logit link)
   AIC   BIC logLik deviance
 299.1 315.5 -145.5    291.1
Random effects:
 Groups Name        Variance Std.Dev.
 area   (Intercept) 0.33441  0.57828 
number of obs: 450, groups: area, 30

Estimated scale (compare to  1 )  0.9462897 

Fixed effects:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -2.33328    0.86467  -2.698  0.00697 ** 
x1           0.36417    0.31776   1.146  0.25178    
x2           0.15127    0.03137   4.821 1.43e-06 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Correlation of Fixed Effects:
   (Intr) x1    
x1 -0.125       
x2 -0.960 -0.050